Workshop of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database, ECML-PKDD 2025

15 September 2025, Porto, Portugal 🇵🇹

Important dates

• Paper submission deadline: 14 June 2025

• Notification of acceptance:14 July 2025

The intersection between deep learning and neuromorphic computing presents a promising avenue for the development of energy-efficient intelligent systems. Deep learning models have shown remarkable performance in a variety of applications, but their widespread deployment is hindered by the high computational costs associated with their training. Neuromorphic computing, inspired by the structure and function of the human brain, offers a promising alternative by leveraging physical artificial neurons to perform computation.

The potential of this intersection lies in its ability to deploy the power of deep learning on intelligent devices that require energy efficiency for working autonomously, enabling a truly pervasive AI. The advantages of neuromorphic hardware over classical digital computing architectures include faster speed, lower power consumption, higher integration density, analog computing, and larger data throughput. This makes neuromorphic hardware an attractive alternative for implementing deep learning models in real-world applications.

The goal of this interdisciplinary workshop is to bring together researchers from different fields such as Computer Science, Engineering, Physics, Materials Science, Control Theory, and Dynamical Systems to share and discuss new exciting ideas at the intersection of deep learning and neuromorphic computing. The workshop will also place a strong emphasis on the application of these technologies in diverse sectors such as robotics, biomedical engineering, and environmental monitoring, encouraging a broader spectrum of practical implementations. 

The workshop will be structured in two tracks. The first track will focus on deep learning concepts for neuromorphic implementations, including reservoir computing, light-weight neural networks, partially untrained structured networks, continuous-time recurrent neural networks, neural ODEs, spiking and oscillatory neural networks, training algorithms beyond backpropagation and unconventional computing paradigms. This track includes the integration of reinforcement learning and genetic algorithms for hardware optimization, addressing interoperability with existing deep learning frameworks and the software scalability challenges. Discussions will also include the ethical, privacy, and societal implications of deploying these technologies, ensuring a holistic view of their impact.

The second track emphasizes neuromorphic hardware for deep learning, including electronic, mechanical, and photonic neuromorphic hardware, in-memory and analogue computing architectures, in-materia computing architectures, integration and scalability of neural networks in hardware, hardware implementation of neuronal and synaptic functions, massively parallel hardware networks, physical computing and quantum computing.

Call for contributions

We cordially invite submissions of original research papers, position papers, and extended abstracts that converge at the intersection of deep learning and neuromorphic computing. We solicit contributions not only focusing on technical aspects, but also considering broader ethical and societal implications of energy-efficiency in AI. 

Topics of interest include, but are not limited to:

• Deep learning concepts for neuromorphic implementations:

  • Reservoir computing, light-weight and semi-randomized neural networks

  • Continuous-time recurrent neural networks and Neural ODEs

  • Spiking neural networks

  • Unconventional computing

  • Advanced Training Algorithms beyond Backpropagation

  • Reinforcement Learning in Neuromorphic Systems

  • Interoperability and Compatibility Challenges

  • Scalability of hardware-friendly Neural Networks design

  • Ethical and societal implications of energy-efficient AI systems

• Neuromorphic hardware for deep learning:

  • Electronic, mechanical, and photonic neuromorphic hardware

  • In-memory and analogue computing architectures

  • In-materia computing architectures

  • Hardware integration of neural networks

  • Hardware implementation of neuronal and synaptic functions

  • Massively parallel hardware networks

  • Quantum computing

  • Physical computing

  • Integration and Scalability of Neural Networks in Hardware

We welcome submissions from researchers working in a variety of fields, including but not limited to Computer Science, Engineering, Physics, Materials Science, Control Theory, and Dynamical Systems. Submissions should clearly demonstrate the relevance to the intersection of deep learning and neuromorphic computing, and highlight the potential impact of the proposed research on the development of energy-efficient intelligent systems.

All submissions will be reviewed by the program committee, and accepted papers will be presented at the workshop. The workshop will feature keynote talks by leading experts in the field, as well as opportunities for discussion and collaboration.

Submission guidelines

• Papers should be written in English and formatted according to the Springer LNCS format. 

• Full papers should be no more than 16 pages in length (including references), while position papers and extended abstracts should be no more than 6 pages in length (including references).

• Submissions should be made through the workshop's CMT submission page.

Papers authors will have the faculty to opt-in or opt-out for publication of their submitted papers in the joint post-workshop proceedings published by Springer Communications in Computer and Information Science, organised by focused scope and possibly indexed by WOS. Notice that novelty is not essential for contributions that will not appear in the workshop proceedings (presentation-only contributions), as we invite abstracts and papers that have already been presented or published elsewhere with the aim of maximizing the dissemination and cross-pollination of ideas among the deep learning and neuromorphic hardware communities. 

Workshop Chairs